Regress poverty on our socialism index. With one more year of
socialist history, poverty changes by:
percentage points (two digits).
R solution -> dont' peak too early ;) !
# Estimate regression models.
ols_1 <- lm_robust(data = Dat, formula = poverty ~ socialist)
modelsummary(list("Poverty" = ols_1), # Nicely-formatted table.
statistic = NULL, # Don't report stat. inference
gof_map = c("nobs", "r.squared"))
tinytable_jm2nek0xlstierqz37s1
Poverty
(Intercept)
11.618
socialist
-0.103
Num.Obs.
145
R2
0.007
Regress poverty on civic and political citizenship rights. With a
one unit-increase in citizenship rights, poverty changes by:
percentage points (two digits).
R solution -> dont' peak too early ;) !
# Estimate regression models.
ols_2 <- lm_robust(data = Dat, formula = poverty ~ citizen_rights)
modelsummary(list("Poverty" = ols_2), # Nicely-formatted table.
statistic = NULL, # Don't report stat. inference
gof_map = c("nobs", "r.squared"))
tinytable_eygvlbvqy01rco5vcm4l
Poverty
(Intercept)
27.127
citizen_rights
-2.154
Num.Obs.
145
R2
0.189
Which of the two is a better model of the variance of poverty
across the world?
The
one with socialism The one with
citizenship rights, as the R2 shows The one
with citizenship rights, as the stronger beta coefficient
shows
Make a scatterplot with civic and political citizenship rights on
the Y-axis and socialism as predictor on the X-axis.
R solution -> dont' peak too early ;) !
ggplot(data = Dat, aes(y = citizen_rights, x = socialist)) +
geom_text(aes(label = country)) +
labs(x = "Our socialism index",
y = "Freedom House index of citizenship rights") +
theme_minimal() +
guides(color = "none")
Regress citizenship rights on socialism. With one more year of
socialism, citizenship rights change by:
R solution -> dont' peak too early ;) !
# Estimate regression models.
ols_3 <- lm_robust(data = Dat, formula = citizen_rights ~ socialist)
modelsummary(list("Citiz. Rights" = ols_3), # Nicely-formatted table.
statistic = NULL, # Don't report stat. inference
gof_map = c("nobs", "r.squared"))
tinytable_hrhxvy89f4djcgffdcyj
Citiz. Rights
(Intercept)
7.862
socialist
-0.052
Num.Obs.
145
R2
0.045
Make a regression table that displays all three models. Based on the
table, discuss with your neighbor: What do the results
suggest about whether there is a freedom-equality trade off?
R solution -> dont' peak too early ;) !
modelsummary(list("Poverty" = ols_1, "Poverty" = ols_2, "Citiz. Rights" = ols_3), # Nicely-formatted table.
gof_map = c("nobs", "r.squared"))
tinytable_b6ysc7qsd3aexa9fzssx
Poverty
Poverty
Citiz. Rights
(Intercept)
11.618
27.127
7.862
(1.674)
(4.290)
(0.331)
socialist
-0.103
-0.052
(0.079)
(0.018)
citizen_rights
-2.154
(0.425)
Num.Obs.
145
145
145
R2
0.007
0.189
0.045